When analyzing data with a logistic regression, an equivalent statistic to R-squared does not exist.  The model estimates from a logistic regression are maximum likelihood estimates arrived at through an iterative process.  They are not calculated to minimize variance, so the OLS approach to goodness-of-fit does not apply.  However, to evaluate the goodness-of-fit of logistic models, several pseudo R-squareds have been developed.   These are "pseudo" R-squareds because they look like R-squared in the sense that they are on a similar scale, ranging from 0 to 1 (though some pseudo R-squareds never achieve 0 or 1) with higher values indicating better model fit, but they cannot be interpreted as one would interpret an OLS R-squared and different pseudo R-squareds can arrive at very different values.  

Tere are some Pseudo R-Squared, such as Efron's ,McFadden's, Cox & Snell, Nagelkerke / Cragg & Uhler's, McKelvey & Zavoina.

see http://www.ats.ucla.edu/stat/mul ... suedo_RSquareds.htm

Tere are some Pseudo R-Squared, such as Efron's ,McFadden's, Cox & Snell, Nagelkerke / Cragg & Uhler's, McKelvey & Zavoina.

2# 081015042

Thx a lot

2# 081015042
请问，Logit回归出来直接报告的是Pseudo R2,怎么确定是什么的Pseudo R2呢？是McFadden's,还是其他的？
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烦请智者赐教，谢谢！